Systems and methods for transferring messaging to automation

ABSTRACT

The present disclosure relates generally to facilitating routing of communications. More specifically, techniques are provided to dynamically transfer messaging between a network device and a terminal device to a type of bot based on intents identified from the messaging. Further, techniques are provided to track performance of the selected type of bot during automation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/987,779, filed Aug. 7, 2020, which claims the benefit of U.S.Provisional Patent Application No. 62/883,994, filed Aug. 7, 2019, allof which are hereby incorporated by reference in their entirety.

FIELD

The present disclosure relates generally to facilitating routing ofcommunications. More specifically, techniques are provided todynamically transfer messaging between a network device and a terminaldevice to a bot and to track performance of the bot during automation.

SUMMARY

The term embodiment and like terms are intended to refer broadly to allof the subject matter of this disclosure and the claims below.Statements containing these terms should be understood not to limit thesubject matter described herein or to limit the meaning or scope of theclaims below. Embodiments of the present disclosure covered herein aredefined by the claims below, not this summary. This summary is ahigh-level overview of various aspects of the disclosure and introducessome of the concepts that are further described in the DetailedDescription section below. This summary is not intended to identify keyor essential features of the claimed subject matter, nor is it intendedto be used in isolation to determine the scope of the claimed subjectmatter. The subject matter should be understood by reference toappropriate portions of the entire specification of this disclosure, anyor all drawings and each claim.

Certain embodiments of the present disclosure include acomputer-implemented method. The method may include receiving a requestfor a conversation. The method may further include determining an intentfor the conversation. The intent may be determined from the request. Themethod may further include, based on the intent, automatically providingan option to transfer the conversation to a bot. When the option isselected, the conversation with the bot may be facilitated. The methodmay further include receiving feedback on the conversation. The methodmay further include applying the feedback to a model that is used todetermine a future intent associated with one or more future requests.

Certain embodiments of the present disclosure include a system. Thesystem may include one or more data processors; and a non-transitorycomputer-readable storage medium containing instructions which, whenexecuted on the one or more data processors, cause the one or more dataprocessors to perform the methods described above and herein.

Certain embodiments of the present disclosure include a computer-programproduct tangibly embodied in a non-transitory machine-readable storagemedium, including instructions configured to cause a data processingapparatus to perform the methods described above and herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described in conjunction with the appendedfigures:

FIG. 1 shows a block diagram of an embodiment of a network interactionsystem;

FIG. 2 shows a block diagram of another embodiment of a networkinteraction system;

FIGS. 3A-3C show block diagrams of other embodiments of a networkinteraction system that includes a connection management system;

FIG. 4 shows a representation of a protocol-stack mapping of connectioncomponents' operation;

FIG. 5 represents a multi-device communication exchange system accordingto an embodiment;

FIG. 6 shows a block diagram of an embodiment of a connection managementsystem;

FIG. 7 shows a block diagram of a network environment for dynamicallyswitching between bots and terminal devices during communicationsessions;

FIG. 8 shows a block diagram representing a communication server;

FIG. 9 shows a block diagram representing a network environment forenhancing endpoint selection using machine-learning techniques;

FIGS. 10A-10G are screenshots of user interfaces for transferringmessaging to automation; and

FIG. 11 is a flowchart of a method for transferring messaging toautomation.

In the appended figures, similar components and/or features can have thesame reference label. Further, various components of the same type canbe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

The ensuing description provides preferred examples of embodiment(s)only and is not intended to limit the scope, applicability orconfiguration of the disclosure. Rather, the ensuing description of thepreferred examples of embodiment(s) will provide those skilled in theart with an enabling description for implementing a preferred examplesof embodiment. It is understood that various changes can be made in thefunction and arrangement of elements without departing from the spiritand scope as set forth in the appended claims.

FIG. 1 shows a block diagram of an embodiment of a network interactionsystem 100 which implements and supports certain embodiments andfeatures described herein. Certain embodiments relate to establishing aconnection channel between a network device 105 (which can be operatedby a user 110) and a terminal device 115 (which can be operated by anagent 120). In certain embodiments, the network interaction system 100can include a client device 130 associated with a client 125.

In certain embodiments, a user 110 can be an individual browsing a website or accessing an online service provided by a remote server 140. Aclient 125 can be an entity that provides, operates, or runs the website or the online service, or individuals employed by or assigned bysuch an entity to perform the tasks available to a client 125 asdescribed herein. The agent 120 can be an individual, such as a supportagent tasked with providing support or information to the user 110regarding the website or online service. Out of a large number ofagents, a subset of agents may be appropriate for providing support orinformation for a particular client 125. The agent 120 may be affiliatedor not affiliated with the client 125. Each agent can be associated withone or more clients 125. In some non-limiting examples, a user 110 canbe an individual shopping an online store from a personal computingdevice, a client 125 can be a company that sells products online, and anagent 120 can be a representative employed by the company. In variousembodiments, the user 110, client 125, and agent 120 can be otherindividuals or entities.

While FIG. 1 shows only a single network device 105, terminal device 115and client device 130, an interaction system 100 can include multiple ormany (e.g., tens, hundreds or thousands) of each of one or more of thesetypes of devices. Similarly, while FIG. 1 shows only a single user 110,agent 120 and client 125, an interaction system 100 can include multipleor many of each of one or more of such entities. Thus, it may benecessary to determine which terminal device is to be selected tocommunicate with a given network device. Further complicating matters, aremote server 140 may also be configured to receive and respond toselect network-device communications.

A connection management system 150 can facilitate strategic routing ofcommunications. A communication can include a message with content(e.g., defined based on input from an entity, such as typed or spokeninput). The communication can also include additional data, such as dataabout a transmitting device (e.g., an IP address, account identifier,device type and/or operating system); a destination address; anidentifier of a client; an identifier of a webpage or webpage element(e.g., a webpage or webpage element being visited when the communicationwas generated or otherwise associated with the communication) or onlinehistory data; a time (e.g., time of day and/or date); and/or destinationaddress. Other information can be included in the communication. In someinstances, connection management system 150 routes the entirecommunication to another device. In some instances, connectionmanagement system 150 modifies the communication or generates a newcommunication (e.g., based on the initial communication). The new ormodified communication can include the message (or processed versionthereof), at least some (or all) of the additional data (e.g., about thetransmitting device, webpage or online history and/or time) and/or otherdata identified by connection management system 150 (e.g., account dataassociated with a particular account identifier or device). The new ormodified communication can include other information as well.

Part of strategic-routing facilitation can include establishing,updating and using one or more connection channels between networkdevice 105 and one or more terminal devices 115. For example, uponreceiving a communication from network device 105, connection managementsystem 150 can first estimate to which client (if any) the communicationcorresponds. Upon identifying a client, connection management system 150can identify a terminal device 115 associated with the client forcommunication with network device 105. In some instances, theidentification can include evaluating a profile of each of a pluralityof agents (or experts or delegates), each agent (e.g., agent 120) in theplurality of agents being associated with a terminal device (e.g.,terminal device 115). The evaluation can relate to a content in anetwork-device message. The identification of the terminal device 115can include a technique described, for example, in U.S. application Ser.No. 12/725,799, filed on Mar. 17, 2010, which is hereby incorporated byreference in its entirety for all purposes.

In some instances, connection management system 150 can determinewhether any connection channels are established between network device105 and a terminal device associated with the client (or remote server140) and, if so, whether such channel is to be used to exchange a seriesof communications including the communication.

Upon selecting a terminal device 115 to communicate with network device105, connection management system 150 can establish a connection channelbetween the network device 105 and terminal device 115. In someinstances, connection management system 150 can transmit a message tothe selected terminal device 115. The message may request an acceptanceof a proposed assignment to communicate with a network device 105 oridentify that such an assignment has been generated. The message caninclude information about network device 105 (e.g., IP address, devicetype, and/or operating system), information about an associated user 110(e.g., language spoken, duration of having interacted with client, skilllevel, sentiment, and/or topic preferences), a received communication,code (e.g., a clickable hyperlink) for generating and transmitting acommunication to the network device 105, and/or an instruction togenerate and transmit a communication to network device 105.

In one instance, communications between network device 105 and terminaldevice 115 can be routed through connection management system 150. Sucha configuration can allow connection management system 150 to monitorthe communication exchange and to detect issues (e.g., as defined basedon rules) such as non-responsiveness of either device or extendedlatency. Further, such a configuration can facilitate selective orcomplete storage of communications, which may later be used, forexample, to assess a quality of a communication exchange and/or tosupport learning to update or generate routing rules so as to promoteparticular post-communication targets.

In some embodiments, connection management system 150 can monitor thecommunication exchange in real-time and perform automated actions (e.g.,rule-based actions) based on the live communications. For example, whenconnection management system 150 determines that a communication relatesto a particular item (e.g., product), connection management system 150can automatically transmit an additional message to terminal device 115containing additional information about the item (e.g., quantity of itemavailable, links to support documents related to the item, or otherinformation about the item or similar items).

In one instance, a designated terminal device 115 can communicate withnetwork device 105 without relaying communications through connectionmanagement system 150. One or both devices 105, 115 may (or may not)report particular communication metrics or content to connectionmanagement system 150 to facilitate communication monitoring and/or datastorage.

As mentioned, connection management system 150 may route selectcommunications to a remote server 140. Remote server 140 can beconfigured to provide information in a predetermined manner. Forexample, remote server 140 may access defined one or more text passages,voice recording and/or files to transmit in response to a communication.Remote server 140 may select a particular text passage, recording orfile based on, for example, an analysis of a received communication(e.g., a semantic or mapping analysis).

Routing and/or other determinations or processing performed atconnection management system 150 can be performed based on rules and/ordata at least partly defined by or provided by one or more clientdevices 130. For example, client device 130 may transmit a communicationthat identifies a prioritization of agents, terminal-device types,and/or topic/skill matching. As another example, client device 130 mayidentify one or more weights to apply to various variables potentiallyimpacting routing determinations (e.g., language compatibility,predicted response time, device type and capabilities, and/orterminal-device load balancing). It will be appreciated that whichterminal devices and/or agents are to be associated with a client may bedynamic. Communications from client device 130 and/or terminal devices115 may provide information indicating that a given terminal deviceand/or agent is to be added or removed as one associated with a client.For example, client device 130 can transmit a communication with IPaddress and an indication as to whether a terminal device with theaddress is to be added or removed from a list identifyingclient-associated terminal devices.

Each communication (e.g., between devices, between a device andconnection management system 150, between remote server 140 andconnection management system 150 or between remote server 140 and adevice) can occur over one or more networks 170. Any combination of openor closed networks can be included in the one or more networks 170.Examples of suitable networks include the Internet, a personal areanetwork, a local area network (LAN), a wide area network (WAN), or awireless local area network (WLAN). Other networks may be suitable aswell. The one or more networks 170 can be incorporated entirely withinor can include an intranet, an extranet, or a combination thereof. Insome instances, a network in the one or more networks 170 includes ashort-range communication channel, such as a Bluetooth or a BluetoothLow Energy channel. In one embodiment, communications between two ormore systems and/or devices can be achieved by a secure communicationsprotocol, such as secure sockets layer (SSL) or transport layer security(TLS). In addition, data and/or transactional details may be encryptedbased on any convenient, known, or to be developed manner, such as, butnot limited to, Data Encryption Standard (DES), Triple DES,Rivest-Shamir-Adleman encryption (RSA), Blowfish encryption, AdvancedEncryption Standard (AES), CAST-128, CAST-256, Decorrelated Fast Cipher(DFC), Tiny Encryption Algorithm (TEA), eXtended TEA (XTEA), CorrectedBlock TEA (XXTEA), and/or RCS, etc.

A network device 105, terminal device 115 and/or client device 130 caninclude, for example, a portable electronic device (e.g., a smart phone,tablet, laptop computer, or smart wearable device) or a non-portableelectronic device (e.g., one or more desktop computers, smartappliances, servers, and/or processors). Connection management system150 can be separately housed from network, terminal and client devicesor may be part of one or more such devices (e.g., via installation of anapplication on a device). Remote server 140 may be separately housedfrom each device and connection management system 150 and/or may be partof another device or system. While each device, server and system inFIG. 1 is shown as a single device, it will be appreciated that multipledevices may instead be used. For example, a set of network devices canbe used to transmit various communications from a single user, or remoteserver 140 may include a server stack.

A software agent or application may be installed on and/or executable ona depicted device, system or server. In one instance, the software agentor application is configured such that various depicted elements can actin complementary manners. For example, a software agent on a device canbe configured to collect and transmit data about device usage to aseparate connection management system, and a software application on theseparate connection management system can be configured to receive andprocess the data.

FIG. 2 shows a block diagram of another embodiment of a networkinteraction system 200. Generally, FIG. 2 illustrates a variety ofcomponents configured and arranged to enable a network device 205 tocommunicate with one or more terminal devices 215. The depicted instanceincludes nine terminal devices 215 included in three local-area networks235.

In some instances, a communication from network device 205 includesdestination data (e.g., a destination IP address) that at least partlyor entirely indicates which terminal device is to receive thecommunication. Network interaction system 200 can include one or moreinter-network connection components 240 and/or one or more intra-networkconnection components 255 that can process the destination data andfacilitate appropriate routing.

Each inter-network connection components 245 can be connected to aplurality of networks 235 and can have multiple network cards installed(e.g., each card connected to a different network). For example, aninter-network connection component 245 can be connected to a wide-areanetwork 270 (e.g., the Internet) and one or more local-area networks235. In the depicted instance, in order for a communication to betransmitted from network device 205 to any of the terminal devices, inthe depicted system, the communication must be handled by multipleinter-network connection components 245.

When an inter-network connection component 245 receives a communication(or a set of packets corresponding to the communication), inter-networkconnection component 245 can determine at least part of a route to passthe communication to a network associated with a destination. The routecan be determined using, for example, a routing table (e.g., stored atthe router), which can include one or more routes that are pre-defined,generated based on an incoming message (e.g., from another router orfrom another device) or learned.

Examples of inter-network connection components 245 include a router 260and a gateway 265. An inter-network connection component 245 (e.g.,gateway 265) may be configured to convert between network systems orprotocols. For example, gateway 265 may facilitate communication betweenTransmission Control Protocol/Internet Protocol (TCP/IP) andInternetwork Packet Exchange/Sequenced Packet Exchange (IPX/SPX)devices.

Upon receiving a communication at a local-area network 235, furtherrouting may still need to be performed. Such intra-network routing canbe performed via an intra-network connection component 255, such as aswitch 280 or hub 285. Each intra-network connection component 255 canbe connected to (e.g., wirelessly or wired, such as via an Ethernetcable) multiple terminal devices 215. Hub 285 can be configured torepeat all received communications to each device to which it isconnected. Each terminal device can then evaluate each communication todetermine whether the terminal device is the destination device orwhether the communication is to be ignored. Switch 280 can be configuredto selectively direct communications to only the destination terminaldevice.

In some instances, a local-area network 235 can be divided into multiplesegments, each of which can be associated with independent firewalls,security rules and network protocols. An intra-network connectioncomponent 255 can be provided in each of one, more or all segments tofacilitate intra-segment routing. A bridge 280 can be configured toroute communications across segments 275.

To appropriately route communications across or within networks, variouscomponents analyze destination data in the communications. For example,such data can indicate which network a communication is to be routed to,which device within a network a communication is to be routed to orwhich communications a terminal device is to process (versus ignore).However, in some instances, it is not immediately apparent whichterminal device (or even which network) is to participate in acommunication from a network device.

To illustrate, a set of terminal devices may be configured so as toprovide similar types of responsive communications. Thus, it may beexpected that a query in a communication from a network device may beresponded to in similar manners regardless to which network device thecommunication is routed. While this assumption may be true at a highlevel, various details pertaining to terminal devices can give rise toparticular routings being advantageous as compared to others. Forexample, terminal devices in the set may differ from each other withrespect to (for example) which communication channels are supported,geographic and/or network proximity to a network device and/orcharacteristics of associated agents (e.g., knowledge bases, experience,languages spoken, availability, general personality or sentiment, etc.).Accordingly, select routings may facilitate faster responses that moreaccurately and/or completely respond to a network-device communication.A complication is that static routings mapping network devices toterminal devices may fail to account for variations in communicationtopics, channel types, agent availability, and so on.

FIGS. 3A-3C show block diagrams of other embodiments of a networkinteraction system 300 a-c that includes a connection management system.Each of the depicted systems 300 a-c show only 2 local-area networks 235for simplicity, though it can be appreciated that embodiments can beextended to expand the number of local-area networks. Each of systems300A-C include a connection management system 350, which can identifywhich terminal device is to communicate with network device 205, canestablish and manage (e.g., maintain or close) connection channels, candetermine whether and when to re-route communications in an exchange,and so on. Thus, connection management system 350 can be configured todynamically, and in real-time, evaluate communications, agentavailability, capabilities of terminal devices or agents, and so on, toinfluence routing determinations.

In FIG. 3A, connection management system 350 is associated with each ofnetwork device 205 and a remote server 340 (e.g., connection managementsystem 350 a is associated with network device 205 and connectionmanagement system 350 b is associated with remote server 340). Forexample, connection management system 350 a and/or connection managementsystem 350 b can be installed or stored as an application on each ofnetwork device 205 and remote server 340, respectively. Execution of theapplication(s) can facilitate, for example, a communication betweennetwork device 205 and remote server 340 to identify a terminal device215 selected to participate in a communication exchange with networkdevice 205. The identification can be made based on one or more factorsdisclosed herein (e.g., availability, matching between a communication'stopic/level of detail with agents' or terminal devices' knowledge bases,predicted latency, channel-type availability, and so on).

A client device 330 can provide client data indicating how routingdeterminations are to be made. For example, such data can include:indications as to how particular characteristics are to be weighted ormatched or constraints or biases (e.g., pertaining to load balancing orpredicted response latency). Client data can also include specificationsrelated to when communication channels are to be established (or closed)or when communications are to be re-routed to a different networkdevice. Client data can be used to define various client-specific rules,such as rules for communication routing and so on.

Connection management system 350 b executing on remote server 340 canmonitor various metrics pertaining to terminal devices (e.g., pertainingto a given client), such as which communication channels are supported,geographic and/or network proximity to a network device, communicationlatency and/or stability with the terminal device, a type of theterminal device, a capability of the terminal device, whether theterminal device (or agent) has communicated with a given network device(or user) before and/or characteristics of associated agents (e.g.,knowledge bases, experience, languages spoken, availability, generalpersonality or sentiment, etc.). Accordingly, connection managementsystem 350 b may be enabled to select routings to facilitate fasterresponses that more accurately and/or completely respond to anetwork-device communication based on the metrics.

In the example depicted in FIG. 3A, a communication exchange betweennetwork device 205 and remote server 340 can facilitate earlyidentification of a destination address. Network device 205 may then usethe destination address to direct subsequent communications. Forexample, network device 205 may send an initial communication to remoteserver 340 (e.g., via one or more inter-network connections and awide-area network), and remote server 340 may identify one or morecorresponding clients. Remote server 340 may then identify a set ofterminal devices associated with the one or more corresponding clientsand collect metrics for those terminal devices. The metrics can beevaluated (e.g., by remote server 340) so as to select a terminal deviceto involve in a communication exchange, and information pertaining tothe terminal device (e.g., an IP address) can be sent to network device205. In some embodiments, remote server 340 may continuously orperiodically collect and evaluate metrics for various terminal devicesand store evaluation results in a data store. In such embodiments, uponidentifying a set of terminal devices associated with the one or morecorresponding clients, remote server 340 can access the storedevaluation results from the data store and select a terminal device toinvolve in the communication exchange based on the stored evaluationresults.

In FIG. 3B, connection management system 350 can be configured to serveas a relay and/or destination address. Thus, for example, a set ofnetwork devices 205 may transmit communications, each identifyingconnection management system 350 as a destination. Connection managementsystem 350 can receive each communication and can concurrently monitor aset of terminal devices (e.g., so as to generate metrics for eachterminal device). Based on the monitoring and a rule, connectionmanagement system 350 can identify a terminal device 215 to which it mayrelay each communication. Depending on the embodiment, terminal devicecommunications may similarly be directed to a consistent destination(e.g., of connection management system 350) for further relaying, orterminal devices may begin communicating directly with correspondingnetwork devices. These embodiments can facilitate efficient routing andthorough communication monitoring.

The embodiment depicted in FIG. 3C is similar to that in FIG. 3B.However, in some embodiments, connection management system 350 isdirectly connected to intra-network components (e.g., terminal devices,intra-network connections, or other).

It will be appreciated that many variations of FIGS. 3A-3C arecontemplated. For example, connection management system 350 may beassociated with a connection component (e.g., inter-network connectioncomponent 245 or intra-network connection component 255) such that anapplication corresponding to connection management system 350 (or partthereof) is installed on the component. The application may, forexample, perform independently or by communicating with one or moresimilar or complementary applications (e.g., executing on one or moreother components, network devices or remotes servers).

FIG. 4 shows a representation of a protocol-stack mapping 400 ofconnection components' operation. More specifically, FIG. 4 identifies alayer of operation in an Open Systems Interaction (OSI) model thatcorresponds to various connection components.

The OSI model can include multiple logical layers 402-414. The layersare arranged in an ordered stack, such that layers 402-412 each serve ahigher level and layers 404-414 is each served by a lower layer. The OSImodel includes a physical layer 402. Physical layer 402 can defineparameters physical communication (e.g., electrical, optical, orelectromagnetic). Physical layer 402 also defines connection managementprotocols, such as protocols to establish and close connections.Physical layer 402 can further define a flow-control protocol and atransmission mode.

A link layer 404 can manage node-to-node communications. Link layer 404can detect and correct errors (e.g., transmission errors in the physicallayer 402) and manage access permissions. Link layer 404 can include amedia access control (MAC) layer and logical link control (LLC) layer.

A network layer 406 can coordinate transferring data (e.g., of variablelength) across nodes in a same network (e.g., as datagrams). Networklayer 406 can convert a logical network address to a physical machineaddress.

A transport layer 408 can manage transmission and receipt quality.Transport layer 408 can provide a protocol for transferring data, suchas a Transmission Control Protocol (TCP). Transport layer 408 canperform segmentation/desegmentation of data packets for transmission andcan detect and account for transmission errors occurring in layers402-406. A session layer 410 can initiate, maintain and terminateconnections between local and remote applications. Sessions may be usedas part of remote-procedure interactions. A presentation layer 412 canencrypt, decrypt and format data based on data types known to beaccepted by an application or network layer.

An application layer 414 can interact with software applications thatcontrol or manage communications. Via such applications, applicationlayer 414 can (for example) identify destinations, local resource statesor availability and/or communication content or formatting. Variouslayers 402-414 can perform other functions as available and applicable.

Intra-network connection components 422, 424 are shown to operate inphysical layer 402 and link layer 404. More specifically, a hub canoperate in the physical layer, such that operations can be controlledwith respect to receipts and transmissions of communications. Becausehubs lack the ability to address communications or filter data, theypossess little to no capability to operate in higher levels. Switches,meanwhile, can operate in link layer 404, as they are capable offiltering communication frames based on addresses (e.g., MAC addresses).

Meanwhile, inter-network connection components 426, 428 are shown tooperate on higher levels (e.g., layers 406-414). For example, routerscan filter communication data packets based on addresses (e.g., IPaddresses). Routers can forward packets to particular ports based on theaddress, so as to direct the packets to an appropriate network. Gatewayscan operate at the network layer and above, perform similar filteringand directing and further translation of data (e.g., across protocols orarchitectures).

A connection management system 450 can interact with and/or operate on,in various embodiments, one, more, all or any of the various layers. Forexample, connection management system 450 can interact with a hub so asto dynamically adjust which terminal devices the hub communicates. Asanother example, connection management system 450 can communicate with abridge, switch, router or gateway so as to influence which terminaldevice the component selects as a destination (e.g., MAC, logical orphysical) address. By way of further examples, a connection managementsystem 450 can monitor, control, or direct segmentation of data packetson transport layer 408, session duration on session layer 410, and/orencryption and/or compression on presentation layer 412. In someembodiments, connection management system 450 can interact with variouslayers by exchanging communications with (e.g., sending commands to)equipment operating on a particular layer (e.g., a switch operating onlink layer 404), by routing or modifying existing communications (e.g.,between a network device and a terminal device) in a particular manner,and/or by generating new communications containing particularinformation (e.g., new destination addresses) based on the existingcommunication. Thus, connection management system 450 can influencecommunication routing and channel establishment (or maintenance ortermination) via interaction with a variety of devices and/or viainfluencing operating at a variety of protocol-stack layers.

FIG. 5 represents a multi-device communication exchange system 500according to an embodiment. System 500 includes a network device 505configured to communicate with a variety of types of terminal devicesover a variety of types of communication channels.

In the depicted instance, network device 505 can transmit acommunication over a cellular network (e.g., via a base station 510).The communication can be routed to an operative network 515. Operativenetwork 515 can include a connection management system 520 that receivesthe communication and identifies which terminal device is to respond tothe communication. Such determination can depend on identifying a clientto which that communication pertains (e.g., based on a content analysisor user input indicative of the client) and determining one or moremetrics for each of one or more terminal devices associated with theclient. For example, in FIG. 5 , each cluster of terminal devices 530a-c can correspond to a different client. The terminal devices may begeographically co-located or disperse. The metrics may be determinedbased on stored or learned data and/or real-time monitoring (e.g., basedon availability).

Connection management system 520 can communicate with various terminaldevices via one or more routers 525 or other inter-network orintra-network connection components. Connection management system 520may collect, analyze and/or store data from or pertaining tocommunications, terminal-device operations, client rules, and/oruser-associated actions (e.g., online activity) at one or more datastores. Such data may influence communication routing.

Notably, various other devices can further be used to influencecommunication routing and/or processing. For example, in the depictedinstance, connection management system 520 also is connected to a webserver 540. Thus, connection management system 520 can retrieve data ofinterest, such as technical item details, and so on.

Network device 505 may also be connected to a web server (e.g.,including a web server 545). In some instances, communication with sucha server provided an initial option to initiate a communication exchangewith connection management system 520. For example, network device 505may detect that, while visiting a particular webpage, a communicationopportunity is available and such an option can be presented.

One or more elements of communication system 500 can also be connectedto a social-networking server 550. Social networking server 550 canaggregate data received from a variety of user devices. Thus, forexample, connection management system 520 may be able to estimate ageneral (or user-specific) behavior of a given user or class of users.

FIG. 6 shows a block diagram of an embodiment of a connection managementsystem 600. A message receiver interface 605 can receive a message. Insome instances, the message can be received, for example, as part of acommunication transmitted by a source device (e.g., housed separatelyfrom connection management system 600 or within a same housing), such asa network device or terminal device. In some instances, thecommunication can be part of a series of communications or a communicateexchange, which can include a series of messages or message exchangebeing routed between two devices (e.g., a network device and terminaldevice). This message or communication exchange may be part of and/ormay define an interaction between the devices. A communication channelor operative channel can include one or more protocols (e.g., routingprotocols, task-assigning protocols and/or addressing protocols) used tofacilitate routing and a communication exchange between the devices.

In some instances, the message can include a message generated based oninputs received at a local or remote user interface. For example, themessage can include a message that was generated based on button or keypresses or recorded speech signals. In one instance, the messageincludes an automatically generated message, such as one generated upondetecting that a network device is presenting a particular app page orwebpage or has provided a particular input command (e.g., key sequence).The message can include an instruction or request, such as one toinitiate a communication exchange.

In some instances, the message can include or be associated with anidentifier of a client. For example, the message can explicitly identifythe client (or a device associated with the client); the message caninclude or be associated with a webpage or app page associated with theclient; the message can include or be associated with a destinationaddress associated with a client; or the message can include or beassociated with an identification of an item (e.g., product) or serviceassociated with the client. To illustrate, a network device may bepresenting an app page of a particular client, which may offer an optionto transmit a communication to an agent. Upon receiving user inputcorresponding to a message, a communication may be generated to includethe message and an identifier of the particular client.

A processing engine 610 may process a received communication and/ormessage. Processing can include, for example, extracting one or moreparticular data elements (e.g., a message, a client identifier, anetwork-device identifier, an account identifier, and so on). Processingcan include transforming a formatting or communication type (e.g., to becompatible with a particular device type, operating system,communication-channel type, protocol and/or network).

A message assessment engine 615 may assess the (e.g., extracted orreceived) message. The assessment can include identifying, for example,one or more categories or tags for the message. Examples of category ortag types can include (for example) topic, sentiment, complexity, andurgency. A difference between categorizing and tagging a message can bethat categories can be limited (e.g., according to a predefined set ofcategory options), while tags can be open. A topic can include, forexample, a technical issue, a use question, or a request. A category ortag can be determined, for example, based on a semantic analysis of amessage (e.g., by identifying keywords, sentence structures, repeatedwords, punctuation characters and/or non-article words); user input(e.g., having selected one or more categories); and/ormessage-associated statistics (e.g., typing speed and/or responselatency).

In some instances, message assessment engine 615 can determine a metricfor a message. A metric can include, for example, a number ofcharacters, words, capital letters, all-capital words or instances ofparticular characters or punctuation marks (e.g., exclamation points,question marks and/or periods). A metric can include a ratio, such as afraction of sentences that end with an exclamation point (or questionmark), a fraction of words that are all capitalized, and so on.

Message assessment engine 615 can store a message, message metric and/ormessage statistic in a message data store 620. Each message can also bestored in association with other data (e.g., metadata), such as dataidentifying a corresponding source device, destination device, networkdevice, terminal device, client, one or more categories, one or morestages and/or message-associated statistics). Various components ofconnection management system 600 (e.g., message assessment engine 615and/or an interaction management engine 625) can query message datastore 620 to retrieve query-responsive messages, message metrics and/ormessage statistics.

An interaction management engine 625 can determine to which device acommunication is to be routed and how the receiving and transmittingdevices are to communicate. Each of these determinations can depend, forexample, on whether a particular network device (or any network deviceassociated with a particular user) has previously communicated with aterminal device in a set of terminal devices (e.g., any terminal deviceassociated with connection management system 600 or any terminal deviceassociated with one or more particular clients).

In some instances, when a network device (or other network deviceassociated with a same user or profile) has previously communicated witha given terminal device, communication routing can be generally biasedtowards the same terminal device. Other factors that may influencerouting can include, for example, whether the terminal device (orcorresponding agent) is available and/or a predicted response latency ofthe terminal device. Such factors may be considered absolutely orrelative to similar metrics corresponding to other terminal devices. Are-routing rule (e.g., a client-specific or general rule) can indicatehow such factors are to be assessed and weighted to determine whether toforego agent consistency.

When a network device (or other network device associated with a sameuser or account) has not previously communicated with a given terminaldevice, a terminal-device selection can be performed based on factorssuch as, for example, an extent to which various agents' knowledge basecorresponds to a communication topic, availability of various agents ata given time and/or over a channel type, types and/or capabilities ofterminal devices (e.g., associated with the client). In one instance, arule can identify how to determine a sub-parameter to one or morefactors such as these and a weight to assign to each parameter. Bycombining (e.g., summing) weighted sub-parameters, a parameter for eachagent can be determined. A terminal device selection can then be made bycomparing terminal devices' parameters.

With regard to determining how devices are to communicate, interactionmanagement engine 625 can (for example) determine whether a terminaldevice is to respond to a communication via (for example) SMS message,voice call, video communication, etc. A communication type can beselected based on, for example, a communication-type priority list(e.g., at least partly defined by a client or user); a type of acommunication previously received from the network device (e.g., so asto promote consistency), a complexity of a received message,capabilities of the network device, and/or an availability of one ormore terminal devices. Appreciably, some communication types will resultin real-time communication (e.g., where fast message response isexpected), while others can result in asynchronous communication (e.g.,where delays (e.g., of several minutes or hours) between messages areacceptable).

Further, interaction management engine 625 can determine whether acontinuous channel between two devices should be established, used orterminated. A continuous channel can be structured so as to facilitaterouting of future communications from a network device to a specifiedterminal device. This bias can persist even across message series. Insome instances, a representation of a continuous channel (e.g.,identifying an agent) can be included in a presentation to be presentedon a network device. In this manner, a user can understand thatcommunications are to be consistently routed so as to promoteefficiency.

In one instance, a parameter can be generated using one or more factorsdescribed herein and a rule (e.g., that includes a weight for each ofthe one or more factors) to determine a connection parametercorresponding to a given network device and terminal device. Theparameter may pertain to an overall match or one specific to a givencommunication or communication series. Thus, for example, the parametermay reflect a degree to which a given terminal device is predicted to besuited to respond to a network-device communication. In some instances,a parameter analysis can be used to identify each of a terminal deviceto route a given communication to and whether to establish, use orterminate a connection channel. When a parameter analysis is used toboth address a routing decision and a channel decision, a parameterrelevant to each decision may be determined in a same, similar ordifferent manner.

Thus, for example, it will be appreciated that different factors may beconsidered depending on whether the parameter is to predict a strengthof a long-term match versus one to respond to a particular messagequery. For example, in the former instance, considerations of overallschedules and time zones may be important, while in the latter instance,immediate availability may be more highly weighted. A parameter can bedetermined for a single network-device/terminal-device combination, ormultiple parameters can be determined, each characterizing a matchbetween a given network device and a different terminal device.

To illustrate, a set of three terminal devices associated with a clientmay be evaluated for potential communication routing. A parameter may begenerated for each that relates to a match for the particularcommunication. Each of the first two terminal devices may havepreviously communicated with a network device having transmitted thecommunication. An input from the network device may have indicatedpositive feedback associated with an interaction with thecommunication(s) with the first device. Thus, a past-interactsub-parameter (as calculated according to a rule) for the first, secondand third devices may be 10, 5, and 0, respectively. (Negative feedbackinputs may result in negative sub-parameters.) It may be determined thatonly the third terminal device is available. It may be predicted thatthe second terminal device will be available for responding within 15minutes, but that the first terminal device will not be available forresponding until the next day. Thus, a fast-response sub-parameter forthe first, second and third devices may be 1, 3 and 10. Finally, it maybe estimated a degree to which an agent (associated with the terminaldevice) is knowledgeable about a topic in the communication. It may bedetermined that an agent associated with the third terminal device ismore knowledgeable than those associated with the other two devices,resulting in sub-parameters of 3, 4 and 9. In this example, the ruledoes not include weighting or normalization parameters (though, in otherinstances, a rule may), resulting in parameters of 14, 11 and 19. Thus,the rule may indicate that the message is to be routed to a device withthe highest parameter, that being the third terminal device. If routingto a particular terminal device is unsuccessful, the message can berouted to a device with the next-highest parameter, and so on.

A parameter may be compared to one or more absolute or relativethresholds. For example, parameters for a set of terminal devices can becompared to each other to identify a high parameter to select a terminaldevice to which a communication can be routed. As another example, aparameter (e.g., a high parameter) can be compared to one or moreabsolute thresholds to determine whether to establish a continuouschannel with a terminal device. An overall threshold for establishing acontinuous channel may (but need not) be higher than a threshold forconsistently routing communications in a given series of messages. Thisdifference between the overall threshold and threshold for determiningwhether to consistently route communication may be because a strongmatch is important in the continuous-channel context given the extendedutility of the channel. In some embodiments, an overall threshold forusing a continuous channel may (but need not) be lower than a thresholdfor establishing a continuous channel and/or for consistently routingcommunications in a given series of messages.

Interaction management engine 625 can interact with an account engine630 in various contexts. For example, account engine 630 may look up anidentifier of a network device or terminal device in an account datastore 635 to identify an account corresponding to the device. Further,account engine 630 can maintain data about previous communicationexchanges (e.g., times, involved other device(s), channel type,resolution stage, topic(s) and/or associated client identifier),connection channels (e.g., indicating—for each of one or moreclients—whether any channels exist, a terminal device associated witheach channel, an establishment time, a usage frequency, a date of lastuse, any channel constraints and/or supported types of communication),user or agent preferences or constraints (e.g., related toterminal-device selection, response latency, terminal-deviceconsistency, agent expertise, and/or communication-type preference orconstraint), and/or user or agent characteristics (e.g., age,language(s) spoken or preferred, geographical location, interests, andso on).

Further, interaction management engine 625 can alert account engine 630of various connection-channel actions, such that account data store 635can be updated to reflect the current channel data. For example, uponestablishing a channel, interaction management engine 625 can notifyaccount engine 630 of the establishment and identify one or more of: anetwork device, a terminal device, an account and a client. Accountengine 635 can (in some instances) subsequently notify a user of thechannel's existence such that the user can be aware of the agentconsistency being availed.

Interaction management engine 625 can further interact with a clientmapping engine 640, which can map a communication to one or more clients(and/or associated brands). In some instances, a communication receivedfrom a network device itself includes an identifier corresponding to aclient (e.g., an identifier of a client, webpage, or app page). Theidentifier can be included as part of a message (e.g., which clientmapping engine 640 may detect) or included as other data in amessage-inclusive communication. Client mapping engine 640 may then lookup the identifier in a client data store 645 to retrieve additional dataabout the client and/or an identifier of the client.

In some instances, a message may not particularly correspond to anyclient. For example, a message may include a general query. Clientmapping engine 640 may, for example, perform a semantic analysis on themessage, identify one or more keywords and identify one or more clientsassociated with the keyword(s). In some instances, a single client isidentified. In some instances, multiple clients are identified. Anidentification of each client may then be presented via a network devicesuch that a user can select a client to communicate with (e.g., via anassociated terminal device).

Client data store 645 can include identifications of one or moreterminal devices (and/or agents) associated with the client. A terminalrouting engine 650 can retrieve or collect data pertaining to each ofone, more or all such terminal devices (and/or agents) so as toinfluence routing determinations. For example, terminal routing engine650 may maintain a terminal data store 655, which can store informationsuch as terminal devices' device types, operating system,communication-type capabilities, installed applications accessories,geographic location and/or identifiers (e.g., IP addresses). Someinformation can be dynamically updated. For example, informationindicating whether a terminal device is available may be dynamicallyupdated based on (for example) a communication from a terminal device(e.g., identifying whether the device is asleep, being turned off/on,non-active/active, or identifying whether input has been received withina time period); a communication routing (e.g., indicative of whether aterminal device is involved in or being assigned to be part of acommunication exchange); or a communication from a network device orterminal device indicating that a communication exchange has ended orbegun.

It will be appreciated that, in various contexts, being engaged in oneor more communication exchanges does not necessarily indicate that aterminal device is not available to engage in another communicationexchange. Various factors, such as communication types (e.g., message),client-identified or user-identified target response times, and/orsystem loads (e.g., generally or with respect to a user) may influencehow many exchanges a terminal device may be involved in.

When interaction management engine 625 has identified a terminal deviceto involve in a communication exchange or connection channel, it cannotify terminal routing engine 650, which may retrieve any pertinentdata about the terminal device from terminal data store 655, such as adestination (e.g., IP) address, device type, protocol, etc. Processingengine 610 can then (in some instances) modify the message-inclusivecommunication or generate a new communication (including the message) soas to have a particular format, comply with a particular protocol, andso on. In some instances, a new or modified message may includeadditional data, such as account data corresponding to a network device,a message chronicle, and/or client data.

A message transmitter interface 660 can then transmit the communicationto the terminal device. The transmission may include, for example, awired or wireless transmission to a device housed in a separate housing.The terminal device can include a terminal device in a same or differentnetwork (e.g., local-area network) as connection management system 600.Accordingly, transmitting the communication to the terminal device caninclude transmitting the communication to an inter- or intra-networkconnection component.

Systems and methods for transferring messaging to automation via a botduring communication sessions with network devices (e.g., operated byusers) are provided. A user may use a network device to initiate aconversation with an agent regarding resolution of an issue. The agentmay launch a widget on the associated terminal device to accelerateresolution of the user's intent. The user's intent may be automaticallyidentified and a recommended automation to transfer to a bot may bemade. Key user information may also be provided, such as order number,account number, and the like. The agent may manually or automaticallyinitiate a transfer to a bot, which allows the agent to provide feedbackon the recommendation to transfer. In addition, the agent may use theterminal device to watch the transferred conversation and rescue theconversation if the user begins to appear dissatisfied. The conversationbetween the user and the bot may appear inline to the conversation withthe agent.

In some embodiments, an agent is able to monitor multiple conversationsthat were transferred to a bot and rescue the conversation if needed. Asentiment score may be utilized to displayed on the terminal device tofacilitate priority of rescuing conversations. Assist features utilizingartificial intelligence such as “intent hint” and “recommendedautomation” may be shown as actionable inline suggestions within theconversation window. The feedback may be used and analyzed in aggregateto apply data science as training input to models.

In some implementations, bots can be configured to autonomouslycommunicate with network devices. Further, bots can be configured for aspecific capability. Examples of capabilities can include updatingdatabase records, providing updates to users, providing additional dataabout the user to agents, determining a user's intent and routing theuser to a destination system based on the intent, predicting orsuggesting responses to agents communicating with users, escalatingcommunication sessions to include one or more additional bots or agents,and other suitable capabilities. In some implementations, while a bot iscommunicating with a network device (e.g., operated by the user) duringa communication session (e.g., using a chat-enabled interface), acommunication server can automatically and dynamically determine toswitch the bot with a terminal device. For example, bots can communicatewith users about certain tasks (e.g., updating a database recordassociated with a user), whereas, terminal devices can communicate withusers about more difficult tasks (e.g., communicating using acommunication channel to solve a technical issue).

In some implementations, determining whether to switch to automationduring a communication session can be based on an analysis of one ormore characteristics of the messages in a communication session.Further, a dynamic sentiment parameter can be generated to represent asentiment of messages, conversations, entities, agents, and so on. Forexample, in cases where the dynamic sentiment parameter indicates thatthe user is frustrated with the bot, the system can automatically switchthe bot with a terminal device so that a live agent can communicate withthe user. See U.S. Ser. No. 15/171,525, filed Jun. 2, 2016, thedisclosure of which is incorporated by reference herein in its entiretyfor all purposes. In some examples, determining whether to switchbetween the bots and terminal devices can be performed without a promptfrom a user. The determination can be performed automatically at thecommunication server based any number of factors, includingcharacteristics of the current messages in the communication session(e.g., chat), characteristics of previous messages transmitted by theuser in previous communication sessions, a trajectory of acharacteristic (e.g., a sentiment) over multiple messages in aconversation, or additional information associated with the user (e.g.,profile information, preference information, and other suitableinformation associated with the user).

FIG. 7 shows a block diagram of a network environment for dynamicallyswitching between bots and terminal devices during communicationsessions. In some implementations, network environment 700 can includenetwork device 705, communication server 710, terminal device 715, andbot 720. Communication server 710 can be a server with one or moreprocessors with at least one storage device, and can be configured toperform methods and techniques described herein. For example,communication server 710 can manage communication sessions betweennetwork devices (e.g., operated by users) and terminal devices (e.g.,operated by agents). Communication server 710 can establish acommunication channel between network device 705 and terminal device 715so that network device 705 and terminal device 715 can communicate witheach other during a communication session. A communication session canfacilitate the exchange of one or more messages between network device705 and terminal device 715. The present disclosure is not limited tothe exchange of messages during a communication session. Other forms ofcommunication can be facilitated by the communication session, forexample, video communication (e.g., a video feed) and audiocommunication (e.g., a Voice-Over-IP connection).

In some implementations, communication server 710 can establish acommunication channel between network device 705 and bot 720. Bot 720can be code that, when executed, is configured to autonomouslycommunicate with network device 705. For example, bot 720 can be a botthat automatically generates messages to initiate conversations with theuser associated with network device 705 and/or to automatically respondto messages from network device 705. In addition, communication server710 can be associated with a platform. Clients (e.g., an external systemto the platform) can deploy different types of bots in their internalcommunication systems using the platform. In some examples, clients canuse their own bots in the platform, which enables clients to implementthe methods and techniques described herein into their internalcommunication systems.

In some implementations, bots can be defined by one or more sources. Forexample, data store 730 can store code representing bots that aredefined (e.g., created or coded) by clients of the communication server.For example, a client that has defined its own bots can load the bots tothe communication server 710. The bots defined by clients can be storedin client bots data store 730. Data store 740 can store coderepresenting bots that are defined by third-party systems. For example,a third-party system can include an independent software vendor. Datastore 750 can store code representing bots that are defined by an entityassociated with communication server 710. For example, bots that arecoded by the entity can be loaded to or accessible by communicationserver 710, so that the bots can be executed and autonomouslycommunicate with users. In some implementations, communication server710 can access bots stored in data store 730, data store 740, and/ordata store 750 using cloud network 760. Cloud network 760 may be anynetwork, and can include an open network, such as the Internet, personalarea network, local area network (LAN), campus area network (CAN),metropolitan area network (MAN), wide area network (WAN), wireless localarea network (WLAN), a private network, such as an intranet, extranet,or other backbone.

In addition, terminal device 715 can be operated by an agent. Terminaldevice 715 can be any portable (e.g., mobile phone, tablet, laptop) ornon-portable device (e.g., electronic kiosk, desktop computer, etc.). Insome instances, the agent can access a website using a browser that isrunning on terminal device 715. For example, the website can include aconsole or platform that is running on the browser of terminal device715. The agent can be logged into the platform using the browser. One ormore login credentials (e.g., username, password, and the like) can beused to authenticate the agent's identity before allowing the agent togain access to the console or web applications included in the console.Examples of a console can include a platform that includes one or moreAPIs (application programming interfaces), a dashboard including one ormore functions, a web-hosted application running on a web browser(without the need for downloading plug-ins) that is capable ofestablishing or joining a communication session, and other suitableinterfaces. Further, the console can include one or more webapplications or functions that can be executed. The web applications orfunctions can be executed at the browser, at communication server 710, alocal server, a remote server, or other suitable computing device. Forexample, the web applications, native applications, or functions canenable an agent to communicate with a user, and to view communicationsbetween the user and one or more bots.

In some implementations, communication server 710 can recommendautomations that cause a conversation to dynamically switch between bot720 and terminal device 715 during a particular communication session.For example, communication server 710 can facilitate a communicationsession between network device 705 and bot 720. Bot 720 can beconfigured to autonomously communicate with network device 705 byexchanging one or more messages with the network device 705 during thecommunication session. Communication server 710 can dynamicallydetermine whether to switch bot 720 with terminal device 715 (or in somecases, vice versa) so that a live agent can communicate with networkdevice 705, instead of bot 720. In some implementations, the switchingcan be performed without a prompt from the network device 705 orterminal device 715. For example, the switching can be based on messageparameters (e.g., scores representing sentiment of a message or seriesof messages) of the messages exchanged between the network device 705and the bot 720, without prompting the network device 705 to request aterminal device.

In some implementations, the communication server 710 utilizes one ormore machine learning models or artificial intelligence to automaticallydetermine whether to switch bot 720 with terminal device 715 (or in somecases, vice versa). For instance, messages exchanged between the networkdevice 705 and the bot 720 may be used as input by the one or moremachine learning models or artificial intelligence to generate anoutput. The output may specify whether the communication session betweenthe network device 705 and the bot 720 is to be switched to a live agentor is to be maintained. The machine learning models may be trained usingsupervised learning techniques. For instance, a dataset of inputmessages and corresponding outputs specifying the appropriate respondingentity (e.g., live agent or bot) can be selected for training of themachine learning models. In some examples, the input messages can beobtained from administrators of the communication server 710, users ofthe network device 705 and other network devices that may interact withthe communication server 710, and/or other sources associated with thecommunication server 710. The machine learning models or artificialintelligence may be evaluated to determine, based on the input messagessupplied to the machine learning models or artificial intelligence,whether the machine learning models or artificial intelligence areproviding useful outputs that can be used to determine whether thecorresponding communication sessions are to be processed by a live agentor a bot. Based on this evaluation, the machine learning models orartificial intelligence may be modified (e.g., one or more parameters orvariables may be updated) to increase the likelihood of the machinelearning models or artificial intelligence generating the desiredresults.

In some implementations, the one or more machine learning models orartificial intelligence may generate, as output, the scores representingsentiment of an input message or series of messages. The communicationserver 710 may determine whether the resulting score exceeds a thresholdvalue corresponding to allocation of a communication session to a bot720 or live agent. For instance, if the score exceeds the thresholdvalue, the communication server 710 may determine that the communicationsession is best suited for a bot 720. Thus, if the communication sessionis currently between a network device 705 and a terminal device 715(e.g., live agent), the communication server 710 may automaticallytransition the communication session from the terminal device 715 to abot 720 or indicate, to the live agent operating the terminal device715, that the bot 720 may handle the communication session with thenetwork device 705. Alternatively, if the score does not exceed thisthreshold value, the communication server 710 may determine that thecommunication session is best suited for a terminal device 715 (e.g.,live agent). In this example, if the communication session is currentlybetween a network device 705 and a bot 720, the communication server 710may automatically transition the communication session from the bot 720to a terminal device 715. In some instances, if the score does notexceed this threshold value, the communication server 710 may indicate,to the live agent operating the terminal device 715, that it shouldintervene in the communication session. It should be noted that thethreshold and scores described above are meant to be illustrative andalternative thresholds and scoring mechanisms may be implemented todetermine whether to utilize a bot 720 or terminal device 715 for aparticular communication session with a network device 705.

In some instances, the communication server 710 may implement a dynamicset of rules or policies that may be used to determine a relevant actionfor a given communication session with the network device 705. The setof rules may be provided by an administrator or other authorized entityassociated with the communication server 710 or may be generated usingone or more machine learning algorithms or artificial intelligence asdescribed above. As an illustrative example, the communication server710 may evaluate the conversational progress between the network device705 and either the bot 720 or terminal device 715 to determine, based onthe set of rules or policies, whether to invoke alternative actions(e.g., such as transitioning the communication session from the bot 720to the terminal device 715 or vice versa). For example, if thecommunication server 710 determines, based on obtained messagesexchanged during the communication session, that there is a flux in theconfidence score for the bot 720 and/or in the sentiment score for theuser of the network device 705, the communication server 710 maydetermine, based on the set of rules or policies relevant to thecommunication session, an appropriate action. This may includetransitioning the communication session from a bot 720 to a terminaldevice 715, transmitting a notification to the terminal device 715indicating that it is to intervene in the communication session, and thelike. Once the relevant action is taken, the communication server 710may continue to monitor subsequent messages exchanged during thecommunication session to evaluate the impact of the action taken subjectto the set of rules or policies. Based on feedback garnered from thesesubsequent messages and/or from the user and live agent post-session,the communication server 710 may update the machine learning algorithmsor artificial intelligence utilized to generate the set of rules orpolicies. These updates may result in the dynamic creation ormodification of the set of rules or policies to provide improvedrecommendations for actions to be taken during a communication session.If the set of rules or policies are defined by an administrator or otherauthorized entity associated with the communication server 710, thecommunication server 710 may provide the obtained feedback and a set ofrecommendations for updating the set of rules or policies to theadministrator or other authorized entity.

In some implementations, communication server 710 can determine toswitch between bot 720 and terminal device 715 automatically based oncharacteristics of the messages exchanged between the bot 720 and thenetwork device 705. In some instances, analyzing the text of a messageto determine the characteristic (e.g., the message parameter) caninclude an analysis of textual or non-textual attributes associated withthe message. For example, communication server 710 can extract one ormore lines of text included in the message from network device 705.Communication server 710 can identify whether the one or more lines oftext include an anchor. Examples of an anchor include a string of textassociated with a polarity (e.g., sentiment or intent, the word“frustrated” corresponding to a negative polarity or frustratedpolarity, the word “happy” corresponding to a positive polarity, and soon). For example, a term “dispute” for one client can be negative, butcan be neutral or positive for a second client. In some instances,anchors can be dynamically determined using supervised machine learningtechniques. For example, one or more clustering algorithms can beexecuted on stored messages to find patterns within the stored messages.The clustered messages can be further filtered and evaluated todetermine the anchor. Further, one or more words near the identifiedanchor can be parsed for amplifiers. An example of an amplifier is aterm that increases or decreases an intensity associated with thepolarity of the anchor, such as “really,” “not really,” “kind of,” andso on. The characteristic can include, for example, the speed of typing,the number of special characters used in the message (e.g., exclamationpoints, question marks, and so on), a semantic analysis of a message(e.g., by identifying keywords, sentence structures, repeated words,punctuation characters and/or non-article words); user input (e.g.,having selected one or more categories); and/or message-associatedstatistics (e.g., response latency).

As a non-limiting example, the message parameter can be a numericalvalue that indicates the high intensity of the negative polarity (e.g.,a message parameter of 20 on a scale of 0-100, with lower numbersindicating a negative polarity and higher numbers indicating a positivepolarity). An algorithm can be used to calculate the message parameter.For example, the algorithm may be based on supervised machine learningtechniques. In a further example, if the term “kind of” is near theanchor “don't like” (e.g., as in the sentence “I kind of don't like”),the term “kind of” may be identified as an amplifier term that indicatesa medium intensity of the negative polarity. In this case, a messageparameter can be generated based on the identification of the mediumintensity of the negative polarity. As a non-limiting example, themessage parameter can be a numerical value that indicates the mediumintensity of the negative polarity (e.g., a message parameter of 40, asopposed to the message parameter of 20). In some instances, the messageparameter can be used to determine which secondary queue is to store thecommunication.

In some implementations, the characteristic of a message can be thesentiment associated with the message. The message parameter canrepresent the sentiment of the message. For example, if the sentiment ofthe message is happy, the message parameter can be a certain value orrange of values, whereas, if the sentiment of the message is angry, themessage parameter can be another value or range of values. Determiningwhether to switch between the bots and the terminal device can be basedon the message parameter, which is continuously and automaticallyupdated with each new message received at communication server 710.

In some implementations, if the communication server 710 determines thata conversation is to be switched from a bot 720 to a terminal device(e.g., live agent), the communication server 710 identifies whichterminal device (e.g., agent) is more likely to positively resolve thetechnical issue presented by the user of the network device 705. Forinstance, based on the characteristics of the messages received from thenetwork device 705 and the technical issue expressed by the user of thenetwork device 705 via its messages, the communication server 710 mayidentify a terminal device associated with an agent that is likely toaddress the technical issue while providing a higher likelihood of apositive interaction with the user.

To identify which agent is best suited for responding to a user for atechnical issue, the communication server 710 may use thecharacteristics of the messages received from the network device 705 andthe technical issue expressed by the user of the network device 705 asinput to a machine learning model or artificial intelligence algorithmconfigured to provide, as output, selection of a particular agent. Themachine learning model or artificial intelligence algorithm may betrained using feedback associated with previously conductedconversations between users and live agents. This feedback may be usedto identify certain characteristics for each agent. Thesecharacteristics may include, but are not limited to, areas of expertiseassociated with technical issues, responsiveness to particularsentiments (e.g., ability to reduce user frustration or anger, etc.),response latency, user satisfaction rating or score, and the like. If anagent is required to intervene in a conversation between a networkdevice 705 and a bot 720, the communication server 710 may use themachine learning model or artificial intelligence algorithm to select aparticular agent that may intervene in the conversation and provide anincreased likelihood of a positive user experience.

In some implementations, the communication server 710 uses feedback fromthe network device 705 to train or update the machine learning model orartificial intelligence algorithm used to select an agent forintervention in a conversation between a network device and a bot. Forinstance, if the network device 705 provides feedback indicating anegative experience with the selected agent, the communication server710 may update the machine learning model or artificial intelligencealgorithm to reduce the likelihood of the agent's selection for aconversation having identical or similar characteristics to theconversation associated with the received feedback. Alternatively, ifthe network device 705 provides feedback indicating a positiveexperience with the selected agent, the communication server 710 mayupdate the machine learning model or artificial intelligence algorithmto further reinforce the agent's ability to positively address identicalor similar conversations.

In some implementations, communication server 710 may recommend orpredict responses to messages received from network device 705. Forexample, communication server 710 can include a message recommendationsystem, which can evaluate messages received from network device 705 anduse a machine-learning model to recommend responses to those receivedmessages. The message recommendation system can display a set ofrecommended messages on terminal device 715 to assist the agent incommunicating with network device 705.

FIG. 8 shows a block diagram of a communication server 805 according tosome embodiments. The communication server 805 may illustrate theinternal components of the communication server 710 of FIG. 7 . Thecommunication server 805 may include a central processing unit (CPU)807, including a processor 810 and memory 815. The communication server805 may further include storage 820.

The CPU 807 may be coupled to a computer-readable medium 825. Thecomputer-readable medium 805 may have any number of modules and engines.Although five modules and engines are illustrated, it is contemplatedthat fewer or greater modules or engines may be implemented in thecomputer-readable medium 825 to perform the functions described herein.As shown in FIG. 8 , the computer-readable medium 825 may include anintent determination engine 827, a bot transfer engine 829, a feedbackmodule 831, a user interface (UI) configuration engine 833, and amachine learning engine 835.

The intent determination engine 827 may be configured to, in conjunctionwith the processor 810, receive a request for a conversation. Therequest may be received from a network device operated by a user. Therequest may be received at a terminal device operated by an agent, forexample. The intent determination engine 827 may further be configuredto, in conjunction with the processor 810, determine an intent for theconversation. The intent may be determined from the request. Forexample, the request may state, “I want my order status.” The intent maybe extracted from the request as “order_status”.

The bot transfer engine 829 may be configured to, in conjunction withthe processor 810, automatically provide one or more options to transferthe conversation to a type of bot from a set of different types of bots.For instance, an option to transfer the conversation to a type of botmay be based on the intent. When this option is selected, theconversation with a bot of the corresponding type may be facilitated.The conversation may be transferred from the terminal device operated bythe agent to the bot, either manually or automatically. Automatictransfer may occur, for example, for an intent with a high confidence ofresolution by the particular type of bot, as determined from machinelearning techniques or past experiences. In some implementations, if thebot transfer engine 829 determines, with high confidence, that an intentmay be resolved by a particular type of bot, the bot transfer engine 824may automatically transfer the conversation from the terminal deviceoperated by the agent to a bot of the particular type without providingoptions to transfer the conversation to a bot of the particular type.This may obviate the need for manual selection of an option tofacilitate the conversation with the bot. In some implementations, thebot transfer agent 829 may utilize a confidence threshold to determinewhether to present the aforementioned one or more options to transferthe conversation to a type of bot. For example, if the bot transferengine 829 determines that a particular intent has a high confidencescore for resolution by a type of bot, and the high confidence scoreexceeds the confidence threshold, the bot transfer engine 829 mayautomatically transfer the conversation to a bot of this particular typewithout presenting an option to transfer the conversation to the bot.Alternatively, if the bot transfer engine 829 determines that aparticular intent has a confidence score for resolution by a type of botthat does not exceed the confidence threshold, the bot transfer engine829 may present the one or more options to transfer the conversation tothe bot.

In some instances, the bot transfer engine 829 may identify one or moretypes of bots based on the intent determined from the request for aconversation. For example, the bot transfer engine 829, in conjunctionwith an intelligent routing system, may implement a confidence scorealgorithm that is used to calculate a confidence score corresponding tothe likelihood or probability of the different types of bots producing asatisfactory response to a given intent. A confidence score may be apercentage or other value where the lower the percentage or value, theless likely the response is a good prediction for an incoming message,and the higher the percentage, the more likely the response is a goodprediction for an incoming message. If the user has expresseddissatisfaction with regard to the conversation with a particular typeof bot, the intelligent routing system may update the confidence scorealgorithm such that, for identical or similar intents, the particulartype of bot is assigned a lower confidence score, thereby serving as anindication that the identical or similar intents are more likely to behandled properly by a terminal device (e.g., live agent) or another typeof bot. As another non-limiting example, if the user has expressedsatisfaction with regard to the conversation with a particular type ofbot, the intelligent routing system may update the confidence scorealgorithm such that, for identical or similar intents, the particulartype of bot is assigned an equal or greater confidence score, therebyserving as an indication that the identical or similar intents are morelikely to be handled properly by the particular type of bot.

In some implementations, the bot transfer engine 829 may dynamicallyprovide one or more options to transfer the conversation to a type ofbot from a terminal device operated by an agent. For instance, via aninterface that may be displayed on a terminal device to recommend botsand receive feedback, the bot transfer engine 829 may provide an agentwith one or more recommendations for types of bots that may be able tohandle the identified intent. Each recommendation may include acorresponding confidence score, which indicates a likelihood that theintent is resolvable using the corresponding type of bot. In addition toproviding a confidence score for each type of bot that may be capable ofhandling the request, the bot transfer engine 829, via the interface,may provide an agent with an option to transfer a communication sessionor conversation to the type of bot. If the agent selects an option totransfer the communication session to a particular type of bot, the bottransfer engine 829 may transfer the communication session to a bot ofthis particular type. The bot transfer engine 829 may select the bot ofthe particular type based on one or more factors. For instance, the bottransfer engine 829 may select a bot of the particular type that hassufficient bandwidth to partake in the communication session with theuser that submitted the request. The bot transfer engine 829 mayadditionally, or alternatively, select a bot of the particular typebased on one or more characteristics of the user. For instance, the bottransfer engine 829 may select a bot of the particular type that isconfigured to communicate in a language of the user.

In some implementations, the bot transfer engine 829 may further providean agent with an option to provide feedback with regard to eachrecommendation for transferring a conversation to a type of bot. Forinstance, if an agent determines that a particular recommendation totransfer the conversation to a particular type of bot is improper (e.g.,the type of bot is not capable of handling the identified intent, theincorrect intent was identified, etc.), the bot transfer engine 824 mayuse this feedback to train or otherwise update the machine learningalgorithms or artificial intelligence utilized to calculate confidencescores and generate recommendations based on the confidence scores forthe different types of bots for a given intent and/or similar intents.This may result in a lower confidence score being assigned to the typeof bot by the machine learning algorithms or artificial intelligence foran identical or similar intent. Alternatively, if the agent selects aparticular type of bot to which a communication session is to betransferred, the bot transfer engine 829 may use this selection as anindication of positive feedback with regard to its recommendation toutilize the particular type of bot. This feedback may be used to trainor otherwise update the machine learning algorithms or artificialintelligence described above to further validate the confidence scoreand recommendations for use of the particular type of bot for identicalor similar intents.

In some implementations, if the agent rejects a particularrecommendation to transfer the conversation to a particular type of bot,the bot transfer engine 829 may use the machine learning algorithms orartificial intelligence to generate one or more new recommendations fortransferring the conversation to other types of bots based on theidentified intent. As noted above, if an agent rejects a particularrecommendation to transfer the conversation to a particular type of bot,the bot transfer engine 829 may use this feedback to train or otherwiseupdate the machine learning algorithms or artificial intelligenceutilized to calculate confidence scores and generate recommendationsbased on the confidence scores for the different types of bots for agiven intent and/or similar intents. The bot transfer engine 829 may usethe updated machine learning algorithms or artificial intelligence toprovide new recommendations to the agent for the identified intent.Thus, the bot transfer engine 829 may dynamically and continuouslyprovide an agent with recommendations for transferring a conversation todifferent types of bots based on the intent and the confidence scoresfor these different types of bots as calculated using the machinelearning algorithms or artificial intelligence.

Different types of bots may be capable of handling certain intents in anautomated fashion. For example, for the intent “order_status”, an orderstatus bot may be capable of pulling order information using a providedorder number, customer name, customer e-mail address, IP address, and/orthe like. Once the order information is determined, an order status maybe ascertained by the order status bot and provided back to the user inthe conversation. As another example, for the intent “payment_status”, apayment status bot may be capable of pulling billing information using aprovided account number, customer name, customer e-mail address, IPaddress, and/or the like. Further, using the billing information, apayment status bot may determine whether the customer has submitted apayment, whether a submitted payment has been processed, and the like.Other types of bots may be capable of handling more complex technicalissues (e.g., troubleshooting hardware or software issues, addressingfraud reports, etc.).

In some embodiments, the conversation between the network device and abot may be displayed at the terminal device to allow an agent operatingthe terminal device to intervene in the conversation. The agent maydecide to intervene in the conversation (or the conversation may beautomatically transferred back to the terminal device) based on acalculated sentiment score of the user's satisfaction with theconversation. For example, a threshold may exist for which the sentimentscore should be above if the user is satisfied with the conversation. Ifthe sentiment score is below a threshold, the conversation may bemanually or automatically transferred back to the terminal device.

The sentiment score may be calculated by the machine learning engine835, which may be configured to utilize messages exchanged during theconversation as input to generate the sentiment score. The machinelearning engine 835 may utilize one or more machine learning models thatare trained using supervised learning techniques. For instance, adataset of input messages and corresponding sentiments and sentimentscores can be selected for training of the machine learning models. Insome examples, the input messages can be obtained from administrators ofthe communication server 805, users, agents, and other sources. The oneor more machine learning models may be evaluated to determine, based onthe input messages supplied to the one or more machine learning models,whether the one or more machine learning models are providing usefuloutputs that can be used to determine the sentiment and correspondingsentiment score for a conversation. Based on this evaluation, the one ormore machine learning may be modified (e.g., one or more parameters orvariables may be updated) to increase the likelihood of the one or moremachine learning models generating the desired results.

In some instances, the one or more machine learning models may utilizeone or more clustering algorithms to determine a sentiment for aparticular conversation between a network device and a bot. For example,the machine learning engine 835 may extract messages exchanged during aconversation between the network device and a bot to identify an anchoror other string of text that may be associated with a polarity. Themachine learning engine 835 may determine whether the extracted messagescorrespond to a particular cluster associated with a known sentiment.The cluster may also be associated with or otherwise used to determine asentiment score of the user's satisfaction with the conversation. Forexample, each cluster, in addition to corresponding to a particularsentiment, may be associated with a particular score that may denote apolarity, as described above.

In some implementations, the conversation between the network device andthe bot may be transferred automatically to the terminal device withoutrequiring the agent operating the terminal device to evaluate theconversation and manually intervene in the conversation. For instance,if the sentiment score for a particular conversation is below asentiment score threshold, the bot transfer engine 829 may automaticallytransfer the conversation from the bot to a terminal device to cause alive agent to intervene in the conversation. In some instances, the bottransfer engine 829 may utilize multiple thresholds to determine whetherto present the conversation at the terminal device to allow an agentoperating the terminal device to intervene in the conversation or toautomatically transfer the conversation to the terminal device. Forinstance, if the sentiment score for a conversation is below a firstsentiment score threshold but not a lower, second sentiment scorethreshold, the bot transfer engine 829 may present the conversation atthe terminal device to allow the agent to intervene in the conversationif so required. However, if the sentiment score is below both the firstand second sentiment score thresholds, the bot transfer engine 829 mayautomatically transfer the conversation to the terminal device for agentintervention.

Feedback module 831 may be configured to, in conjunction with theprocessor 810, receive feedback on the conversation. The feedback may beprovided by an agent operating the terminal device. The feedback may bereflective of how well the bot identified the intent and/or handled therequest expressed by the user on the network device. Additionally, thefeedback module 831 may obtain feedback from the user engaged in theconversation. For instance, at the end of a conversation, the feedbackmodule 831 may transmit, to the network device utilized by a user, arequest to provide feedback with regard to its conversation with the botand/or live agent. This request may be provided in the form of a survey,through which the user may indicate its sentiment with regard to theconversation, as well as provide a performance evaluation of the botand/or live agent. In some instances, the feedback module 831 may obtainfeedback from the user engaged in the conversation during theconversation. For instance, the feedback module 831 may evaluate userresponses during the conversation to determine the sentiment of theuser. For example, the feedback module 831 may analyze the textual ornon-textual attributes associated with user messages to identify whetherthe user messages include an anchor associated with a polarity. As anillustrative example, if the user indicates, in a message, that it is“frustrated” or indicates that the proposed solution is incorrect (e.g.,a “no” response, etc.), the feedback module 831 may determine that themessage represents a negative polarity and, thus, determine that the botfailed to identify the intent and/or handle the request expressed by theuser in a satisfactory manner. The feedback may be received in anysuitable form. For example, the feedback may be expressed on a letterscale, on a number scale, through words, through selection of icons orgraphics, and/or the like. The interface displayed to the agent and/oruser to provide the feedback and to render the views of the conversationmay be handled by the user interface configuration engine 833 inconjunction with the processor 810.

The machine learning engine 835 may be configured to, in conjunctionwith the processor 810, feed the conversation, identified intent, andprovided feedback into a database and analyze the data to drawinferences about how well a type of bot and/or live agent handled theconversation. This data, along with other historical conversation dataand feedback, may be used to build a model that may be used to determinea future intent associated with one or more future requests. Forexample, if a particular type of bot successfully handled an“order_status” intent to the satisfaction of a user, future“order_status” intents may also be transferred to a bot of theparticular type. In addition, future requests stating “I want my orderstatus” may be automatically correlated with the “order_status” intentbased on positive feedback. However, if a particular type of botunsuccessfully handled an intent to the dissatisfaction of the user,future intents similar to the aforementioned intent may be transferredto a terminal device in order for these future intents to be handled bya live agent or to another type of bot that may be better suited tohandle the intent based on a confidence score for the other type of botand calculated based on the intent.

As an example, the first time a user contacts an agent with an intent of“delivery_status”, the user may be automatically redirected to an orderstatus bot. An agent may provide feedback on one or more aspects of theinteraction, such as whether the order status bot was the appropriatebot to which to transfer the conversation, and whether or not the orderstatus bot was capable of resolving the intent. Further, the user mayalso provide feedback on similar or different aspects of theinteraction, such as whether the order status bot successfully resolvedthe user's intent, whether the order status bot was responsive to theuser's messages, whether responses from the order status bot wererelevant to the intent and/or to the particular messages submitted bythe user, and the like. Other techniques may be used to collect feedbackregarding the interaction without explicit feedback from the agentand/or the user, such as by observing whether or not the agent had tointervene in the conversation, which may indicate that the order statusbot was not the correct bot, that the conversation should not have beenautomatically transferred, or that the order status bot was not capableof resolving the intent. The feedback may be used to train a model thatmay be applied to future interactions. For example, if it is ascertainedthat the order status bot was not the appropriate bot, but that anothertype of bot, such as a shipping status bot, was the appropriate bot, anobserved “delivery_status” intent in the future may be transferred to ashipping status bot instead. Feedback may again be collected on theinteraction between the user and the shipping status bot to even furtherrefine the model applied to future interactions.

FIG. 9 shows a block diagram representing network environment 900 forenhancing endpoint selection (i.e., a terminal device or a bot) usingmachine-learning techniques. Network environment 900 may include networkdevice 905 (operated by a user) communication server 910, bot 915 andterminal device 920. Communication server 910 can facilitate theestablishment of a communication channel that enables network device 905and at least one bot 915 and terminal device 920 to communicate.

Communication server 910 may include intelligent routing system 925,message recommendation system 930, and message data store 935. Each ofintelligent routing system 925 and message recommendation system 930 mayinclude one or more computing devices with a processor and a memory thatexecute instructions to implement certain operations. In someimplementations, intelligent routing system 925 may be a bot configuredto intelligently route communications received from network devices tothe appropriate destination. Intelligent routing system 925 may includeone or more processors configured to execute code that causes one ormore machine-learning techniques or artificial intelligence techniquesto intelligently route messages. In some implementations, intelligentrouting system 925 can execute one or more machine-learning techniquesto train a model that predicts whether a message received from networkdevice 905 may be successfully addressed by a bot 915.

As a non-limiting example, intelligent routing system 925 may receive amessage from network device 905 through a communication channelestablished or facilitated by communication server 910 (e.g., a nativeapplication configured to enable users to communicate with each otheracross various devices). Intelligent routing system 925 may evaluate theincoming message according to certain embodiments described above. Forexample, intelligent routing system 925 may evaluate the content (e.g.,text, audio clips, images, emoticons, or other suitable content)included in the received message using a trained machine-learning model.The content of the message can be inputted into the machine-learningmodel to generate a predicted destination (e.g., a particular terminaldevice or bot). The machine-learning model may be continuously trainedbased on feedback signal 940 received from network device 905. In someimplementations, intelligent routing system 925 may request anacknowledgement from network device 905 of the predicted destination. Asa non-limiting example, intelligent routing system 925 may evaluate themessage using a machine-learning technique, and a result of theevaluation may include a prediction that bot 915 is the destination forthe message. To confirm, intelligent routing system 925 mayautomatically request feedback signal 940. For example, feedback signal940 may include a request for network device 905 to acknowledge whetherbot 915 is the correct destination for the message (e.g., “Is TechnicalSupport the correct destination?”). If network device 905 transmits theacknowledgement that bot 915 is the correct destination (e.g., thedestination intended by the user operating network device 905), thenintelligent routing system 925 may train the machine-learning model topredict that future messages including the exact or similar content(e.g., a threshold of similarity, such as 10 percent difference incontent) as the received message are to be routed to bot 915. However,if intelligent routing system 925 receives feedback signal 940indicating that bot 915 is not the correct or intended destination forthe received message, but rather terminal device 920 is the correct orintended destination, intelligent routing system 925 can train themachine-learning model that future messages including the exact orsimilar content as the received message are to be routed to terminaldevice 920 (instead of bot 915). In some implementations, intelligentrouting system 925 may not immediately update or train themachine-learning model to route future messages to terminal device 920,but rather, intelligent routing system 925 may wait for a thresholdnumber of incorrect routings to bot 915 before routing all futuremessages with the exact same or similar content as the received messageto terminal device 920. As a non-limiting example, intelligent routingsystem 925 may begin routing future messages (that were predicted to berouted to bot 915) to terminal device 920 instead of bot 915 after fiveinstances of network devices transmitting feedback signals indicatingthat bot 915 is not the correct or intended destination.

In some implementations, the intelligent routing system 925 obtainsfeedback signals 940 from the network device 905 after completion of aconversation with a bot 915 or terminal device 920. For instance, at theconclusion of a conversation between a user and the bot 915 or agentusing the terminal device 920, the intelligent routing system 925 mayrequest feedback with regard to the performance of the bot 915 and/orterminal device 920 (e.g., live agent) in handling the intent expressedby the user. The network device 905 may provide, in response to thisrequest, a feedback signal 940 that indicates a user's satisfaction withthe handling of the intent by the bot 915 and/or terminal device 920during the course of the user's conversation. Based on this feedbackfrom the user, the intelligent routing system 925 may update or trainthe machine learning model. For instance, if the user has expresseddissatisfaction with regard to the conversation with a bot 915, theintelligent routing system 925 may update or train the machine learningmodel to route future messages having identical or similar intents tothe terminal device 920. As another non-limiting example, if the userhas expressed satisfaction with regard to the conversation with a bot915, the intelligent routing system 925 may reinforce the machinelearning model to increase the likelihood of routing future messageshaving identical or similar intents to the bot 915.

In some implementations, the intelligent routing system 925 mayimplement a confidence score algorithm that is used to calculate aconfidence score corresponding to the likelihood or probability of thebot 915 producing a satisfactory response to a given intent. Aconfidence score may be a percentage or other value where the lower thepercentage or value, the less likely the response is a good predictionfor an incoming message, and the higher the percentage, the more likelythe response is a good prediction for an incoming message. If the userhas expressed dissatisfaction with regard to the conversation with a bot915, the intelligent routing system 925 may update the confidence scorealgorithm such that, for identical or similar intents, the bot 915 isassigned a lower confidence score, thereby serving as an indication thatthe identical or similar intents are more likely to be handled properlyby the terminal device 920. As another non-limiting example, if the userhas expressed satisfaction with regard to the conversation with a bot915, the intelligent routing system 925 may update the confidence scorealgorithm such that, for identical or similar intents, the bot 915 isassigned an equal or greater confidence score, thereby serving as anindication that the identical or similar intents are more likely to behandled properly by the bot 915.

In some implementations, the intelligent routing system 925 usesfeedback signals 940 from the network device 905 after completion of aconversation with a bot 915 or terminal device 920 to determine whetherthe network device 905 and the intelligent routing system 925 providedincorrect responses with regard to a destination for a message from thenetwork device 905. For example, if the intelligent routing system 925initially predicts that bot 915 is the destination for a receivedmessage, and the network device 905 provides acknowledgement of thisbeing the correct destination for the message, but later it isdetermined (such as through later feedback from the network device 905or as a result of an agent having to intervene in the conversation) thatthe bot 915 was not the correct destination for the received message,the intelligent routing system 925 may update or train the machinelearning model to route future messages having identical or similarintents to the terminal device 920. Further, the intelligent routingsystem 925 may forego using acknowledgements with regard to predictionscorresponding to identical or similar intents to train the machinelearning model and may instead await post-conversation feedback from thenetwork device 905 to train or update the machine learning model.

Message data store 935 may store some or all messages received in thepast from one or more network devices. Further, message data store 935may also store some or all messages transmitted by terminal devices orbots during previous communication sessions with network devices.Message data store 935 may also store some or all messages transmittedby network devices to bots during communication sessions. Further,message data store 935 may store some or all messages transmitted bybots to network devices during communication sessions. In someimplementations, message data store 935 may be a database of allmessages processed (e.g., transmitted by or received at) communicationserver 910.

Message recommendation system 930 may analyze the database of messagesstored at message data store 935. In some implementations, messagerecommendation system 930 may evaluate the messages stored at messagedata store 935 using one or more machine-learning algorithms orartificial intelligence algorithms. For example, message recommendationsystem 930 may execute one or more clustering algorithms, such asK-means clustering, means-shift clustering, Density-Based SpatialClustering of Applications with Noise (DBSCAN) clustering,Expectation-Maximization (EM) Clustering using Gaussian Mixture Models(GMM), and other suitable machine-learning algorithms, on the databaseof messages stored in message data store 935. In some implementations, arecurrent neural network (RNN) or a convolutional neural network (CNN)may be used to predict response messages to assist the agent. In someimplementations, message recommendation system 930 may use supportvector machines (SVM), supervised, semi-supervised, ensemble techniques,or unsupervised machine-learning techniques to evaluate all previousmessages to predict responses to incoming messages received from networkdevices during communication sessions. For example, messagerecommendation system 930 may evaluate the content of messages receivedfrom network devices (or messages received at communication server 910from bots or terminal devices) and compare the results of the evaluationto the one or more clusters of previous messages stored in message datastore 935. Once the cluster is identified, message recommendation system930 can identify the most relevant response messages based on aconfidence threshold. For example, an incoming message (e.g., receivedat communication server 910 from network device 905) may correspond to atechnical issue based on the content of the incoming message. Messagerecommendation system 930 can identify that the incoming messagecorresponds to a technical issue based on an evaluation of the contentof the incoming message (e.g., text evaluation). Message recommendationsystem 930 can access message data store 935 to identify the cluster ofmessages associated with technical issues. Message recommendation system930 can select one or more response messages within the cluster ofmessages based on a confidence threshold. As a non-limiting example, aconfidence algorithm can be executed to generate a confidence score. Aconfidence score may be a percentage value where the lower thepercentage, the less likely the response is a good prediction for theincoming message, and the higher the percentage, the more likely theresponse is a good prediction for the incoming message. A minimumconfidence threshold may be defined as a measure of certainty ortrustworthiness associated with each discovered pattern. Further, anexample of a confidence algorithm may be the Apriori Algorithm,similarity algorithms indicating similarity between two data sets, andother suitable confidence algorithms.

In some implementations, the message recommendation system 930 providesthe most relevant response messages to the bot 915 or terminal device920 (e.g., live agent) for use in the conversation with the networkdevice 905. The bot 915 may select, from the response messages providedby the message recommendation system 930, the response message havingthe highest confidence score. The bot 915 may provide this response tothe network device 905 as part of the conversation. In some instances,the message recommendation system 930 may monitor the conversation toidentify a reaction to the selected response message by the user of thenetwork device 905. For instance, the message recommendation system 930may use the reaction (e.g., messages submitted in response to theselected response message) to further train the one or more clusteringalgorithms. As an illustrative example, if the bot 915 submits aresponse message that is negatively received by the user of the networkdevice 905, the message recommendation system 930 may update theclustering algorithms to reduce the likelihood (e.g., confidence score)of the particular response message being selected for an identical orsimilar incoming message from a network device. Alternatively, if thebot 915 submits a response message that is positively received by theuser of the network device 905, the message recommendation system 930may update the clustering algorithms to further reinforce selection ofthe particular response message for identical or similar incomingmessages.

If the most relevant response messages are provided to a terminal device920, these most relevant response messages may be presented to an agentusing the terminal device 920 for selection and incorporation into aconversation between the terminal device 920 and the network device 905.The agent may elect to select a particular response message for use inthe conversation or ignore the response messages presented to the agent.If the agent selects a response message from those provided by themessage recommendation system 930, the message recommendation system 930may monitor the conversation to identify a reaction to the selectedresponse message by the user of the network device 905, as describedabove. However, if the agent ignored the response messages provided bythe message recommendation system 930 and provides its own responsemessage to the network device 905, the message recommendation system 930may record this response message and associate this response message tothe identified technical issue. Further, the message recommendationsystem 930 may monitor the conversation to determine the user'ssentiment with regard to the submitted response message. If the responsemessage submitted by the agent is positively received by the user, themessage recommendation system 930 may update the clustering algorithmsto associate the response message with a cluster corresponding to theidentified technical issue. However, if the response message submittedby the agent is negatively received by the user, the messagerecommendation system 930 may evaluate the response message to determinewhether the response message can be associated with an alternativecluster or otherwise disassociate the response message from the clustercorresponding to the identified technical issue.

FIG. 10A illustrates an exemplary screen shot of an interface that maybe displayed on a terminal device in accordance with some embodiments. Auser-initiated conversation may appear on a portion of a screen. Forexample, the user may operate a network device to say “Hi. Where is myorder?” The terminal device and/or a bot may analyze the request todetermine an intent, e.g., order status. The order status bot may bedisplayed as a widget that recommends that the conversation betransferred to automation. The widget may further display a confidencethat the intent may be successfully addressed by the bot. For instance,the widget may display a confidence score or rating that may beindicative of the confidence that the intent may be successfullyaddressed by the bot. As illustrated in FIG. 10A, the confidence scoreor rating for the order status bot is presented as being “High,” whichmay denote a high likelihood that the order status bot may successfullyaddress the intent. In some instances, the widget may further display anexplanation or justification for selection of the particular bot for theintent. For instance, as illustrated in FIG. 10A, the order status botis selected because the user explicitly said “Where is my order?”. Thismay provide an agent with appropriate context with regard to theselection of the particular bot. The widget may further display aninteractive element (e.g., a button) allowing the agent to transfer theconversation to a bot, as well as other interactive elements allowingthe agent to provide feedback on the recommendation. Although shown asbeing initiated as a manual transfer, it is contemplated that in someembodiments, the conversation may be automatically transferred to thebot.

In some implementations, the terminal device through which the interfaceis displayed may be selected from among a set of terminal devices (e.g.,agents) based on one or more characteristics of the request submitted bythe user. For instance, a terminal device may be selected based on acorresponding agent's likelihood to address the determined intent in apositive manner that leads to a positive user experience. For example,as described above, the characteristics of the messages received from anetwork device and the technical issue expressed by the user of thenetwork device may be used as input to a machine learning model orartificial intelligence algorithm configured to provide, as output,selection of a particular agent. The machine learning model orartificial intelligence algorithm may select a particular agent that mayprovide an increased likelihood of a positive user experience. Theinterface may be provided to the terminal device associated with theselected agent such that the selected agent is presented with the userrequest and the aforementioned widget.

FIG. 10B illustrates an exemplary screen shot of an interface that maybe displayed on a terminal device in order to receive feedback inaccordance with some embodiments. For the request, “Where is my order?”,two automations may be recommended: an order status bot and a paymentbot. An agent may select negative feedback on the payment bot and selectthat the payment bot doesn't fit the user's intent. This feedback may beused by a machine learning engine to better refine which bots arerecommended for transfer in the future.

FIG. 10C illustrates an exemplary screen shot of an interface that maybe displayed on a terminal device to transfer a conversation toautomation in accordance with some embodiments. An agent operating theterminal device may select an interactive element (e.g., a button) witha transfer option in order to transfer the conversation from theterminal device to a bot.

FIG. 10D illustrates an exemplary screen shot of an interface that maybe displayed on a terminal device to recommend different types of botsand receive feedback according to some embodiments. A request may bereceived stating “Where is my order? I was charged several days ago.”Two different types of bots may be recommended to which to transfer theconversation: an order status bot and a payment bot. Although therequest does discuss payment, the intent of the conversation is aboutorder status and not payment. Thus, the agent may provide negativefeedback on the recommendation to transfer the conversation to a paymentbot. The negative feedback may be used by a machine learning engine tobetter ascertain future intents and to make better recommendations forautomations.

In an embodiment, each recommendation further includes a confidencescore corresponding to the likelihood that a particular type of bot willbe able to handle the identified intent in a satisfactory manner. Asnoted above, a confidence score algorithm may be used to calculate aconfidence score corresponding to the likelihood or probability of aparticular type of bot producing a satisfactory response to anidentified intent. As illustrated in FIG. 10D, the confidence score orrating for an order status bot is presented as being “High,” which maydenote a high likelihood that an order status bot may successfullyaddress an identified intent. Similarly, the confidence score or ratingfor a payment bot is presented as being “High” for a different intent.

In some instances, the agent may be provided with an explanation orjustification for presentation of the different types of bots for theidentified one or more intents. For instance, as illustrated in FIG.10D, an order status bot is selected because the user explicitly said“Where is my order?”. Further, a payment bot is selected because theuser explicitly said “I was charged several days ago,” This may providean agent with appropriate context with regard to the identification ofthe different types of bots in response to a given request from a userfrom which different intents were identified. The widget may furtherdisplay an interactive element (e.g., a button) allowing the agent totransfer the conversation to a bot, as well as other interactiveelements allowing the agent to provide feedback on the recommendations.

As noted above, the agent may provide negative feedback on arecommendation to transfer the conversation to a particular type of bot.The negative feedback may be used by a machine learning engine to betterascertain future intents and to make better recommendations for types ofbots that may be capable to handle these future intents. For instance,if the agent indicates that a particular type of bot is not suited tohandle the actual intent associated with a particular message or requestsubmitted by a user, the feedback may be used to train or otherwiseupdate one or more machine learning algorithms or artificialintelligence configured to identify, from a given communication session,the intent of a user and, based on this intent, one or more types ofbots that may be able to handle the intent. In some instances, the oneor more machine learning algorithms or artificial intelligence may beupdated such that a confidence score for the particular type of botdetermined to be ill-suited for a particular intent is reduced for theparticular type of bot for identical or similar intents.

In some implementations, the recommendations provided via the interfaceare selected based on the corresponding confidence scores of thedifferent types of bots for the identified intents. For instance, aparticular recommendation presented via the interface may correspond toa particular type of bot that has a highest confidence score for anidentified intent. In some instances, recommendations may be providedfor different types of bots having confidence scores that exceed athreshold value for a particular intent. This may provide an agent withadditional options for selection of a particular type of bot for anobtained request.

FIG. 10E illustrates an exemplary screen shot of an interface that maybe displayed on a terminal device after a conversation has beentransferred to automation according to some embodiments. The agent mayselect the interactive element corresponding to transfer in order tomove the conversation to the order status bot. However, the conversationbetween the user and the bot may still be displayed on the terminaldevice so that the agent may “rescue” the conversation if needed, i.e.,intervene for any reason, such as dissatisfaction by the user. Forinstance, if the agent determines that the user is responding negativelyto the response messages provided by the bot, the agent may select aninteractive element of the bot widget to intervene in the conversationand provide its own response messages to the user. In some instances, acommunication server, as described above, may also monitor theconversation between the user and the bot to determine whether the agentis required to intervene in the conversation. For example, if thecommunication server determines that the user is responding negativelyto the response messages submitted by the bot, the communication servermay update the bot widget to indicate a reduced confidence rating orscore for the bot (e.g., change the confidence from “High” to a lowervalue, etc.). This may serve as an indication to the agent that thebot's performance is degrading with regard to the particularconversation. In some instances, if the confidence rating or score forthe bot falls below a particular threshold value, the communicationserver may automatically transfer the conversation to the agent. Throughthe interface, the communication server may alert the agent of thistransfer, which may prompt the agent to intervene in the conversation.

FIG. 10F illustrates an entire conversation displayed between the userand the bot. FIG. 10G illustrates an alternate screen shot of aninterface that may be displayed on a terminal device with automationrecommendations. In FIG. 10G, a confidence level is displayed as“relevance” and an accompanying percentage. In addition to theconfidence level, the interface may include one or more interactiveelements that may be used by an agent to indicate whether a botrecommendation is appropriate for the identified intent. For instance,as illustrated in FIG. 10G, the interface may include, for eachautomation (e.g., bot) recommendation, a “vote up” button and a “votedown” button. Selection of the “vote up” button for a particularrecommendation may serve as an indication that the selection of theparticular bot for the identified intent is appropriate or correct. Thisfeedback may be used to train or update a machine learning model orartificial intelligence algorithm to further reinforce selection of theparticular bot for identical or similar intents. However, selection ofthe “vote down” button for a particular recommendation may serve as anindication that the selection of the particular bot for the identifiedintent is inappropriate or incorrect. This feedback may be used to trainor update the machine learning model or artificial intelligencealgorithm to decrease the likelihood of selection of the particular botfor identical or similar intents.

The interface may further include a drop down menu or other option toallow an agent to provide a justification for selection of a vote downbutton for a particular recommendation. The drop down menu or otheroption may include one or more pre-defined responses that the agent mayselect from. Additionally, or alternatively, the interface may includean option for an agent to provide its own justification for selection ofthe vote down button or forego providing a justification altogether. Aresponse provided by the agent may be used to further train or updatethe machine learning model or artificial intelligence algorithm suchthat the particular bot is less likely to be selected for the particularintent.

FIG. 11 is a flowchart of a method for transferring messaging toautomation. At step 1105, a request for a conversation may be received.The request may be initiated by at a network device by a user seekingassistance or some other conversation with an agent operating a terminaldevice. The request may be received in natural language, by selection ofan interactive element, or by any other suitable method.

At step 1110, an intent for the conversation may be determined. Theintent may be determined by analyzing the request. For example, if therequest is received in natural language, it may be parsed to determinekeywords in the request and to disregard other language. In anotherexample, the request may be compared to a database of intents todetermine the closest intent to the request. In some embodiments,machine learning may be used to identify intents associated withrequests.

At step 1115, based on the intent, an option to transfer theconversation to a bot may be automatically provided. When the option isselected, the conversation with the bot may be facilitated. For example,if the intent is one that has been successfully handled in the past by abot (as determined from a machine learning model of feedback received inthe past), an option may be automatically provided on a terminal deviceto transfer that conversation based on a high probability of success.The probability of success may be expressed as a confidence score orrelevance score, as discussed further herein, and may be presented inany suitable form. In some embodiments, during the conversation, asentiment score may be calculated based on the conversation between thenetwork device and the bot. The sentiment score may be a scorereflective of the user's satisfaction with the conversation. In someembodiments, if the sentiment score is below a threshold, theconversation may be transferred back to the terminal device.

At step 1120, feedback on the conversation may be received. For example,an agent may continue to watch the conversation between the user of anetwork device and the bot and may provide feedback on various aspectsof the interaction. For example, the agent may provide feedback on theidentification of the intent based on the language of the request. Theagent may further provide feedback on whether or not a recommendation totransfer a conversation to automation is appropriate, and/or whether ornot a recommendation to transfer a conversation to a particular bot isappropriate. The agent may further provide feedback on how well a botaddressed the request based on, for example, user satisfaction.

At step 1125, the feedback may be applied to a model that is used todetermine a future intent associated with one or more future requests.For example, feedback on identification of the intent from the requestmay be used to further refine a model used to identify intents accordingto machine learning. Feedback on whether to transfer a conversation toautomation may be used to identify future conversations that should orshould not be transferred to automation. Feedback on whether to transfera conversation to a specific bot may be used to identify whether thatparticular bot should be recommended in the future, and so on and soforth.

Specific details are given in the above description to provide athorough understanding of the embodiments. However, it is understoodthat the embodiments can be practiced without these specific details.For example, circuits can be shown as block diagrams in order not toobscure the embodiments in unnecessary detail. In other instances,well-known circuits, processes, algorithms, structures, and techniquescan be shown without unnecessary detail in order to avoid obscuring theembodiments.

Implementation of the techniques, blocks, steps and means describedabove can be done in various ways. For example, these techniques,blocks, steps and means can be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitscan be implemented within one or more application specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that portions of the embodiments can be described as aprocess which is depicted as a flowchart, a flow diagram, a data flowdiagram, a structure diagram, or a block diagram. Although a flowchartcan describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations can be re-arranged. A process is terminatedwhen its operations are completed, but could have additional steps notincluded in the figure. A process can correspond to a method, afunction, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

Furthermore, embodiments can be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks can bestored in a machine readable medium such as a storage medium. A codesegment or machine-executable instruction can represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment can becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. can be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, ticket passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies can beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions can be used in implementing themethodologies described herein. For example, software codes can bestored in a memory. Memory can be implemented within the processor orexternal to the processor. As used herein the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium”, “storage” or“memory” can represent one or more memories for storing data, includingread only memory (ROM), random access memory (RAM), magnetic RAM, corememory, magnetic disk storage mediums, optical storage mediums, flashmemory devices and/or other machine readable mediums for storinginformation. The term “machine-readable medium” includes, but is notlimited to portable or fixed storage devices, optical storage devices,wireless channels, and/or various other storage mediums capable ofstoring that contain or carry instruction(s) and/or data.

While the principles of the disclosure have been described above inconnection with specific apparatuses and methods, it is to be clearlyunderstood that this description is made only by way of example and notas limitation on the scope of the disclosure.

What is claimed is:
 1. A computer-implemented method, comprising:determining an intent associated with a conversation, wherein messagesare exchanged in real-time during the conversation, and wherein theconversation is associated with a terminal device; receiving themessages in real-time as the messages are exchanged; using the intentand the messages as input to a trained machine learning model togenerate a recommended response message, wherein the trained machinelearning model identifies a cluster of messages based on the intent, andwherein the recommended response message is generated as a result of aconfidence score associated with the recommended response messagesatisfying a confidence threshold; providing the recommended responsemessage, wherein when the recommended response is received at theterminal device, an agent associated with the terminal device determineswhether to communicate the recommended response message or analternative response message; dynamically determining feedbackcorresponding to an actual response message exchanged during theconversation, wherein the actual response message includes either therecommended response message or the alternative response message;determining a polarity for the conversation based on the feedback; andupdating the trained machine learning model using the intent, the actualresponse message, and the polarity to determine new recommended responsemessages, wherein the trained machine learning model is updated bymodifying the identified cluster of messages according to the actualresponse message, the polarity, and the feedback.
 2. Thecomputer-implemented method of claim 1, further comprising: recordingmessages corresponding to the agent during the conversation; andassociating the messages corresponding to the agent with the intentbased on the polarity for the conversation.
 3. The computer-implementedmethod of claim 1, wherein the terminal device is selected based on alikelihood of the terminal device to provide a positive experienceduring the conversation.
 4. The computer-implemented method of claim 1,wherein the feedback corresponding to the conversation includes textualand non-textual attributes associated with the messages exchanged duringthe conversation.
 5. The computer-implemented method of claim 1, whereinthe intent is associated with a request for the conversation, andwherein the request is in a natural language.
 6. Thecomputer-implemented method of claim 1, wherein the polarity for theconversation is determined using another trained machine learning model,and wherein the other trained machine learning model is trained todetermine the polarity as the messages are exchanged in theconversation.
 7. The computer-implemented method of claim 1, wherein thefeedback is dynamically determined by monitoring the conversation toidentify a reaction to the actual response message, and wherein thereaction is used to determine the polarity for the conversation.
 8. Asystem, comprising: one or more processors; and memory storing thereoninstructions that, as a result of being executed by the one or moreprocessors, cause the system to: determine an intent associated with aconversation, wherein messages are exchanged in real-time during theconversation, and wherein the conversation is associated with a terminaldevice; receive the messages in real-time as the messages are exchanged;use the intent and the messages as input to a trained machine learningmodel to generate a recommended response message, wherein the trainedmachine learning model identifies a cluster of messages based on theintent, and wherein the recommended response message is generated as aresult of a confidence score associated with the recommended responsemessage satisfying a confidence threshold; provide the recommendedresponse message, wherein when the recommended response is received atthe terminal device, an agent associated with the terminal devicedetermines whether to communicate the recommended response message or analternative response message; dynamically determine feedbackcorresponding to an actual response message exchanged during theconversation, wherein the actual response message includes either therecommended response message or the alternative response message;determine a polarity for the conversation based on the feedback; andupdate the trained machine learning model using the intent, the actualresponse message, and the polarity to determine new recommended responsemessages, wherein the trained machine learning model is updated bymodifying the identified cluster of messages according to the actualresponse message, the polarity, and the feedback.
 9. The system of claim8, wherein the instructions further cause the system to: record messagescorresponding to the agent during the conversation; and associate themessages corresponding to the agent with the intent based on thepolarity for the conversation.
 10. The system of claim 8, wherein theterminal device is selected based on a likelihood of the terminal deviceto provide a positive experience during the conversation.
 11. The systemof claim 8, wherein the feedback corresponding to the conversationincludes textual and non-textual attributes associated with the messagesexchanged during the conversation.
 12. The system of claim 8, whereinthe intent is associated with a request for the conversation, andwherein the request is in a natural language.
 13. The system of claim 8,wherein the polarity for the conversation is determined using anothertrained machine learning model, and wherein the other trained machinelearning model is trained to determine the polarity as the messages areexchanged in the conversation.
 14. The system of claim 8, wherein thefeedback is dynamically determined by monitoring the conversation toidentify a reaction to the actual response message, and wherein thereaction is used to determine the polarity for the conversation.
 15. Anon-transitory, computer-readable storage medium storing thereonexecutable instructions that, as a result of being executed by one ormore processors of a computer system, cause the computer system to:determine an intent associated with a conversation, wherein messages areexchanged in real-time during the conversation, and wherein theconversation is associated with a terminal device; receive the messagesin real-time as the messages are exchanged; use the intent and themessages as input to a trained machine learning model to generate arecommended response message, wherein the trained machine learning modelidentifies a cluster of messages based on the intent, and wherein therecommended response message is generated as a result of a confidencescore associated with the recommended response message satisfying aconfidence threshold; provide the recommended response message, whereinwhen the recommended response is received at the terminal device, anagent associated with the terminal device determines whether tocommunicate the recommended response message or an alternative responsemessage; dynamically determine feedback corresponding to an actualresponse message exchanged during the conversation, wherein the actualresponse message includes either the recommended response message or thealternative response message; determine a polarity for the conversationbased on the feedback; and update the trained machine learning modelusing the intent, the actual response message, and the polarity todetermine new recommended response messages, wherein the trained machinelearning model is updated by modifying the identified cluster ofmessages according to the actual response message, the polarity, and thefeedback.
 16. The non-transitory, computer-readable storage medium ofclaim 15, wherein the executable instructions further cause the computersystem to: record messages corresponding to the agent during theconversation; and associate the messages corresponding to the agent withthe intent based on the polarity for the conversation.
 17. Thenon-transitory, computer-readable storage medium of claim 15, whereinthe terminal device is selected based on a likelihood of the terminaldevice to provide a positive experience during the conversation.
 18. Thenon-transitory, computer-readable storage medium of claim 15, whereinthe feedback corresponding to the conversation includes textual andnon-textual attributes associated with the messages exchanged during theconversation.
 19. The non-transitory, computer-readable storage mediumof claim 15, wherein the intent is associated with a request for theconversation, and wherein the request is in a natural language.
 20. Thenon-transitory, computer-readable storage medium of claim 15, whereinthe polarity for the conversation is determined using another trainedmachine learning model, and wherein the other trained machine learningmodel is trained to determine the polarity as the messages are exchangedin the conversation.
 21. The non-transitory, computer-readable storagemedium of claim 15, wherein the feedback is dynamically determined bymonitoring the conversation to identify a reaction to the actualresponse message, and wherein the reaction is used to determine thepolarity for the conversation.